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merged variational posterior changes

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Max Zwiessele 2014-02-26 08:30:27 +00:00
commit d29fa56af2
43 changed files with 1424 additions and 1936 deletions
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21
GPy/kern/_src/add.py
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@ -45,9 +45,6 @@ class Add(Kern):
def update_gradients_full(self, dL_dK, X):
[p.update_gradients_full(dL_dK, X[:,i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
[p.update_gradients_sparse(dL_dKmm, dL_dKnm, dL_dKdiag, X[:,i_s], Z[:,i_s]) for p, i_s in zip(self._parameters_, self.input_slices)]
def gradients_X(self, dL_dK, X, X2=None):
"""Compute the gradient of the objective function with respect to X.
@ -70,13 +67,13 @@ class Add(Kern):
return sum([p.Kdiag(X[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)])
def psi0(self, Z, mu, S):
def psi0(self, Z, variational_posterior):
return np.sum([p.psi0(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)],0)
def psi1(self, Z, mu, S):
def psi1(self, Z, variational_posterior):
return np.sum([p.psi1(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
def psi2(self, Z, mu, S):
def psi2(self, Z, variational_posterior):
psi2 = np.sum([p.psi2(Z[:, i_s], mu[:, i_s], S[:, i_s]) for p, i_s in zip(self._parameters_, self.input_slices)], 0)
# compute the "cross" terms
@ -104,7 +101,7 @@ class Add(Kern):
raise NotImplementedError, "psi2 cannot be computed for this kernel"
return psi2
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
from white import White
from rbf import RBF
#from rbf_inv import RBFInv
@ -127,10 +124,10 @@ class Add(Kern):
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], mu[:,is2], S[:,is2]) * 2.
p1.update_gradients_variational(dL_dKmm, dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], S[:,is1], Z[:,is1])
p1.update_gradients_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], S[:,is1], Z[:,is1])
def gradients_Z_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
from white import White
from rbf import RBF
#from rbf_inv import rbfinv
@ -154,10 +151,10 @@ class Add(Kern):
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(Z[:,is2], mu[:,is2], S[:,is2]) * 2.
target += p1.gradients_z_variational(dL_dKmm, dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], S[:,is1], Z[:,is1])
target += p1.gradients_z_variational(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], S[:,is1], Z[:,is1])
return target
def gradients_muS_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
from white import white
from rbf import rbf
#from rbf_inv import rbfinv
@ -182,7 +179,7 @@ class Add(Kern):
eff_dL_dpsi1 += dL_dpsi2.sum(1) * p2.psi1(z[:,is2], mu[:,is2], s[:,is2]) * 2.
a, b = p1.gradients_muS_variational(dL_dkmm, dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], s[:,is1], z[:,is1])
a, b = p1.gradients_qX_expectations(dL_dpsi0, eff_dL_dpsi1, dL_dpsi2, mu[:,is1], s[:,is1], z[:,is1])
target_mu += a
target_S += b
return target_mu, target_S